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Maximizing learning without sacrificing the fun: Stealth assessment, adaptivity and learning supports in educational games.

Authors :
Shute, Valerie
Rahimi, Seyedahmad
Smith, Ginny
Ke, Fengfeng
Almond, Russell
Dai, Chih‐Pu
Kuba, Renata
Liu, Zhichun
Yang, Xiaotong
Sun, Chen
Source :
Journal of Computer Assisted Learning; Feb2021, Vol. 37 Issue 1, p127-141, 15p
Publication Year :
2021

Abstract

In this study, we investigated the validity of a stealth assessment of physics understanding in an educational game, as well as the effectiveness of different game‐level delivery methods and various in‐game supports on learning. Using a game called Physics Playground, we randomly assigned 263 ninth‐ to eleventh‐grade students into four groups: adaptive, linear, free choice and no‐treatment control. Each condition had access to the same in‐game learning supports during gameplay. Results showed that: (a) the stealth assessment estimates of physics understanding were valid—significantly correlating with the external physics test scores; (b) there was no significant effect of game‐level delivery method on students' learning; and (c) physics animations were the most effective (among eight supports tested) in predicting both learning outcome and in‐game performance (e.g. number of game levels solved). We included student enjoyment, gender and ethnicity in our analyses as moderators to further investigate the research questions. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
02664909
Volume :
37
Issue :
1
Database :
Complementary Index
Journal :
Journal of Computer Assisted Learning
Publication Type :
Academic Journal
Accession number :
148185153
Full Text :
https://doi.org/10.1111/jcal.12473